PROJECT SUMMARY Adaptive optics (AO) ophthalmoscopy allows non-invasive visualization of microscopic retinal structures by correcting the optical blur that is unique to each eye, potentially enabling improving the understanding and management of eye disease. Lack of standardization, however, has hindered the adoption of this technology in the multi-center studies that are the gold standard for testing novel treatments. The overarching goal of this project is to improve rigor and reproducibility in AO ophthalmoscopy, in order to materialize its potential through three specific aims: Aim 1. To develop, build and distribute calibrated model eyes that allow precise compensation of image distortions and scaling errors caused by the optics of AO ophthalmoscopes. These model eyes will be designed to be reproducible by others and have similar optical properties to those of an average human eye. Aim 2. To develop imaging protocols and algorithms that use whole-eye optical biometry to allow precise compensation of image distortions and scaling errors caused by the unique optics of each eye. Aim 3. To collect an open normative dataset of photoreceptor mosaic images in subjects free of eye disease, and to use it to test two hypotheses. First, that rod photoreceptors are lost to aging at a rate of ~1% per year in the central portion of the retina that is affected in various leading blinding conditions, such as age-related macular degeneration. Second, that rod photoreceptor spacing increases with cell loss, offsetting the decline in density, and thus can be used for detecting early signs of disease. The image scaling and distortion correction methods from Aims 1 & 2 will improve our ability to perform these tests. The deliverables of this project will incorporate feedback and testing by the AO retinal imaging community. At this project?s conclusion, users of AO ophthalmoscopes will receive software and calibrated model eyes for precise image scaling and correction distortion, as well as the most anatomically truthful and accurately scaled photoreceptor normative dataset. The proposed practices will facilitate the development of retinal imaging biomarkers that improve early diagnosis and management of eye disease, as well as testing of novel therapies.